Efficient encoding of large antigenic spaces by epitope prioritization with Dolphyn.

We investigate a relatively underexplored component of the gut-immune axis by profiling the antibody response to gut phages using Phage Immunoprecipitation Sequencing (PhIP-Seq). To cover large antigenic spaces, we develop Dolphyn, a method that uses machine learning to select peptides from protein sets and compresses the proteome through epitope-stitching. Dolphyn compresses the size of a peptide library by 78% compared to traditional tiling, increasing the antibody-reactive peptides from 10% to 31%. We find that the immune system develops antibodies to human gut bacteria-infecting viruses, particularly E.coli-infecting Myoviridae. Cost-effective PhIP-Seq libraries designed with Dolphyn enable the assessment of a wider range of proteins in a single experiment, thus facilitating the study of the gut-immune axis.

Efficient encoding of large antigenic spaces by epitope prioritization with Dolphyn.

We investigated a relatively underexplored component of the gut-immune axis by profiling the antibody response to gut phages using Phage Immunoprecipitation Sequencing (PhIP-Seq). To enhance this approach, we developed Dolphyn, a novel method that uses machine learning to select peptides from protein sets and compresses the proteome through epitope-stitching. Dolphyn improves the fraction of gut phage library peptides bound by antibodies from 10% to 31% in healthy individuals, while also reducing the number of synthesized peptides by 78%. In our study on gut phages, we discovered that the immune system develops antibodies to bacteria-infecting viruses in the human gut, particularly E.coli-infecting Myoviridae. Cost-effective PhIP-Seq libraries designed with Dolphyn enable the assessment of a wider range of proteins in a single experiment, thus facilitating the study of the gut-immune axis.

Multi-ligand functionalized particle design for cell targeting and drug delivery.

Particle-based delivery systems encompass some of the most promising techniques for therapeutic drug delivery. In particular, multi-functional nanovector systems permit diverse functions such as efficient drug/imaging agent loading and unloading and increased target specificity. To enhance the efficiency of delivery systems, particle size and shape can be altered and specific ligands can be conjugated to the particles to promote interactions with receptors expressed on target cells. Moreover, to maximize efficiency and specificity, multiple types of ligands can be conjugated to the particle surface. To analyze the multi-ligand-receptor mediated adhesion process, we developed a stochastic model considering diverse biophysical parameters, including non-specific interactions, ligand-receptor specific interactions, kinetic affinity between ligand and receptor, hydrodynamic force and particle size. The results demonstrate that limited contact area restricts the probability of adhesion such that multiple ligand-receptor pairs do not always show enhanced adhesion characteristics. To optimize the effect of multiple ligand-receptor pairs, biophysical parameters must be considered.